Image difference detection

ABSTRACT

The described technology is generally directed towards comparing two images for content differences, such as images based on the frames of a show and a derivative version of that show. Frame pairs such as from an original show and its derivative version are processed into image pairs, which can include decoding, scaling, luminance extraction and/or filtering. An edge pixel image is obtained via edge detection for each image. Edge pixels in one image are compared against a counterpart edge pixel (and possibly neighboring pixels) in the other image to determine matches (matching edge pixels) and mismatches. An image with too many errors based on the mismatches is deemed as a candidate for further review. A difference image can be generated to assist a reviewer in detecting where the mismatches were detected. By repeating for the various frames, a show can be automatically compared against its derivative for content differences.

COPYRIGHT DISCLAIMER

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

During the post production of a show such as a movie or televisionprogram, variations (derivative versions) of the show are typicallycreated, such as a high definition (1920×1080) resolution versionderived from an ultra high definition (3840×2160) resolution original,for example. Other variations of a show can be based on different colorspaces, different EOTFs (electro optical transfer functions) and so on.

Any differences between the variations of a show tend to bedisapprovingly noticed by the viewing audience, as viewers may recollectand/or discuss with others past viewings where a key object or face waspresent in an original scene, but did not appear in the derivativeversion. For example, if scaling or panning is performed, such as isoften done with a lower definition resolution version derived from ahigher definition resolution version, something significant (e.g., aface, a bottle and so forth) can be missing in the derived version.Further, something such as a misspelling in the credits or other textcan be fixed in the original, but this fix does not always gettransferred to a previously derived copy.

Peak signal-to-noise-ratio comparisons and the like do not do very wellin identifying such differences. Moreover, such techniques do not workin different domains, including different resolutions. Thus, a presentsolution is to have one or more human reviewers watch a show and itsderivative version side by side, and note where differences areobserved, so that they can be fixed by the studio or the like thatregenerates the different variations. This manual solution is resourceintensive and tedious, and thus also can result in both subtle andsignificant differences being missed.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology described herein is illustrated by way of example and notlimited in the accompanying figures in which like reference numeralsindicate similar elements and in which:

FIG. 1 is an example block diagram representation of components thatprovide edge images for content comparison, in accordance with variousaspects and example implementations of the subject disclosure.

FIG. 2 is an example representation of a source image and an edge pixelimage obtained therefrom, in accordance with various aspects and exampleimplementations of the subject disclosure.

FIG. 3 is an example representation of an error image generated from thedifferences between two images, overlaid on an actual image, inaccordance with various aspects and example implementations of thesubject disclosure.

FIGS. 4 and 5 comprise a flow diagram of example operations that may beperformed to compare two images for content differences, in accordancewith various aspects and example implementations of the subjectdisclosure.

FIGS. 6 and 7 comprise a flow diagram showing example operations relatedto searching a counterpart image for a matching edge pixel andgenerating a difference image, in accordance with various aspects andexample implementations of the subject disclosure.

FIG. 8 is a flow diagram of example operations that may be performed toprocess a difference image into match and mismatch data, in accordancewith various aspects and example implementations of the subjectdisclosure.

FIG. 9 is a flow diagram of example operations that determine and reporton differences in images obtained from frames, in accordance withvarious aspects and example implementations of the subject disclosure.

FIG. 10 is a flow diagram of example operations that compare edge pixelimages for content differences, in accordance with various aspects andexample implementations of the subject disclosure.

FIG. 11 is a flow diagram of example operations that can be used tocompare the pixels in edge pixel images to determine differences betweenthem, in accordance with various aspects and example implementations ofthe subject disclosure.

FIG. 12 is a block diagram representing an example computing environmentinto which aspects of the subject matter described herein may beincorporated.

FIG. 13 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact, inaccordance with various aspects and implementations of the subjectdisclosure.

DETAILED DESCRIPTION

Various aspects described herein are generally directed towards atechnology in which two images (e.g., corresponding frames of a show anda derivative show) are compared for content in a manner that is notinfluenced by the size or color of the image. To this end, edgedetection provides converted frames of primary (e.g., original) andsecondary (e.g., derived) video content that a computer can match up andscore. Rather than detecting differences in brightness or relativecolor, edge detection results in detecting image differences, includinga font change or missing object.

Before edge detection and matching, the video content can be filtered toremove noise, such as unnoticeable to a viewer but possibly otherwisedetected by the automated comparison process. Further, scaling can beperformed to get the frames to be the same size and resolution so edgepixels can be matched.

Following edge detection, the edge pixels in each frame areautomatically compared, which can be expanded using a searchingtechnique that compares a selected edge pixel in one frame with itscounterpart pixel (and possibly neighboring pixels) in the other frame,to look for a similar edge. In one implementation, the count ofmismatches to the count of matches provides a ratio that can be isevaluated to determine whether a threshold ratio is exceeded.Alternatively, the ratio can be based on the number of mismatches to thetotal number of edge pixels. Those frames with too many edge differencesare automatically reported. In this way, a human observer or team ofobservers can quickly advance to the frames (or scenes of multipleframes) where the differences can be confirmed as needing to be fixed ordeemed not significant.

Further, following pixel matching, a visible representation of any twoframes deemed different can be provided to guide the reviewer in seeingthe differences. For example, the two edge pixel frames (e.g., withwhite pixels showing edges, and black pixels showing non-edges), can beoverlaid on one another, with the mismatched edge pixels shown asdifferent color pixels, such as red. Thus, not only can a reviewerreceive a report as to which frame (or scene of frames) to furtherinspect, but the technology can assist the reviewer in honing in on thedetected differences.

It should be understood that any of the examples herein arenon-limiting. For instance, a higher resolution image can be scaled downto a lower resolution, or vice-versa, and thus which video content isthe primary image (which may or may not be the original) and which isthe secondary image (which may or may not be the derivative but can bethe original) is interchangeable as used herein. As another alternative,it is possible to use techniques such as scene detection to avoid havingto match every frame, although the technology described herein thatmatches frame-by-frame is highly efficient and consistent compared tomanual viewing, and can find differences in as little as a single frame.As such, the technology described herein is not limited to anyparticular embodiments, aspects, concepts, structures, functionalities,values or examples described herein. Rather, any of the embodiments,aspects, concepts, structures, functionalities or examples describedherein are non-limiting, and the present technology may be used invarious ways that provide benefits and advantages in image differencedetection in general.

FIG. 1 is a generalized block diagram representation of an examplesystem 100 in which the video frames 102 of content A are to becompared, e.g., on a frame-by-frame basis as described herein, with thevideo frames 103 of content B. In general, content B's video frames 103have been obtained from a variation previously derived from content A,or vice versa. It is also feasible that content A and content B are bothderivative versions of some other video content, although not having amaster source could lead to both derivative versions having the samemismatch in a given frame, which would not be detected as a mismatch.

In the example of FIG. 1, as represented by respective blocks 104 and105 each frame of the content A's video frames 102 are decoded, as arethose of content B's video frames 103. Blocks 106 and 107 representscaling the images to match, although note that only one of the imagesmay need to be scaled to match the other. In this way, the images areresolution independent; for example, the images can be automaticallyscaled to UHD to ensure a high resolution. Notwithstanding, the imagesizes can be scaled to other values if desired, such as to a lowerresolution with fewer pixels to process, to thereby obtain fasterresults.

The following shows example scaling code:

// scaling frag, printf(“\nScaling processing %d %d %d %d ”, image->h,image->w, orows, ocols);  fflush(stdout);  for(x=0; x < ocols; x++) {  for(y=0; y < orows; y++)    float gx = x / (float)(ocols) *((image->w)−1);    float gy = y / (float)(orows) * (image->h−1);    //assemble picture    float result = interp(image, gx, gy); //interpolation call    putpixel(dst, x, y, result, 1, 0);   }  }

Blocks 108 and 109 represent further preliminary preparation of theimages, namely luminance (Y) extraction and filtering. Moreparticularly, as part of preparing the images, the images can beconverted to luminance (Y), as color contains no real image information.For example, if a frame shows grass, whether the grass tends moretowards green or brown does not change the fact that the images containgrass. The same process can be applied to the other image. Variationscan be applied to produce desired images, e.g., Lift, Gamma, Gain and soon can be varied and applied. In one implementation, the setting(s) areonly done once for each source type rather than for each frame.

With respect to filtering, such as after luminance (Y) is extracted, afilter can be applied to help reduce noise that can be confusing withrespect to the actual picture content. As one example, a show may havefilm grain, which will cause subsequent edge detection to actdifferently in the presence of noise. Thus, a filter can be applied toremove some of the noise; sampling the picture and removing some of thenoise improves the edge quality. For example, high definition resolutionimages are generally different from standard definition resolutionimages, as high definition resolution can be peaking at 4000 nits whilestandard definition resolution may only hit 100 nits. This differencecan be dealt with via a certain amount of automatic adjustment. In oneimplementation, the following filter code can be used:

// filter frag void averagefilter(unsigned char **xc, unsigned char**map, int nrows, int ncols)  {   image *collection = (image*)malloc(sizeof(image));   collection->h = nrows;   collection->w =ncols;   collection->pixels = (unsigned short *)malloc(sizeof(unsigned  short) * ncols * nrows);   image *dst = (image*)malloc(sizeof(image));   dst->h = 5;   dst->w = 5;   dst->pixels =(unsigned short *)malloc(sizeof(unsigned short) *   5 * 5);   int k = 0;  int i, j;    for(i = 0; i < nrows; i++)     {     for(j = 0; j <ncols; j++)      {       k = j + (i * ncols);      collection->pixels[k] = xc[i][j] ;      }     }  for(i=5;i<nrows−5; i++)   {   for(j=5; j<ncols−5; j++)    {    window(collection, dst, j, i, j+5, i+5);     map[i−2][j−2] =average(dst->pixels, 25);    }   } // bubble_sort(dst->pixels, 25);free(dst->pixels); free(collection); free(dst); }

In one implementation, the window function, called by the filter code,sets the window in which a desired edge is obtained:

// function, obtain a group of pixels // image is a structure, *pixels,int width, int height void window(image *src, image *dst, int x0, inty0, int x1, int y1) {  int srcPos = 0;  int dstPos = 0;  int width = 0; int height = 0;   if(x0>x1) {        fprintf( stderr, “FAILED infunction %s of file        %s on line %d:\nx0 > x1 %d %d\n”, _FUNCTION_,_FILE_, _LINE_, x0, x1);         return;   }   if(y0>y1) {       fprintf( stderr, “FAILED in function %s of file        %s on line%d:\ny0 > y1\n”, _FUNCTION_, _FILE_, _LINE_ );         return;   } if(x1−x0 < 0) {        fprintf( stderr, “FAILED in function %s of file       %s on line %d:\nx1−x0 < 0\n”, _FUNCTION_, _FILE_, _LINE_ );        return;   }   if(y1−y0 < 0) {        fprintf( stderr, “FAILED infunction %s of file        %s on line %d:\ny1−y0 > dst->h\n”,_FUNCTION_, _FILE_, _LINE_ );         return;    }    for(int h=y0; h<y1; h++){      width = 0;     for(int w=x0; w<x1; w++) {       srcPos =w + (h * (src->w));       dstPos = width + (height * dst->w);      dst->pixels[dstPos] = src->pixels[srcPos];       width++;      }    height++;    }   return;  }

Following noise reduction by Y-extraction and filtering as describedherein, an edge detection process is applied to each image, asrepresented by blocks 110 and 111. Any edge detector can be used,although in one implementation the well-known Sobel Edge detectorprocess has been found to provide suitable results.

As part of the edge detection is process, initial results can be visiblyas well as automatically reviewed, and further adjustments can beapplied if needed until a suitably defined edge is obtained. Forexample, if not enough edge pixels exist in an image following edgedetection to provide a workable image, adjustments can be made toincrease the number of edge pixels. The result is two edge(edge/non-edge) source images 112 and 113, in which, for example, edgesare represented via white pixels and non-edges are represented via blackpixels.

By way of example, FIG. 2 shows a side by side representation of part ofa source image 218 alongside part of a modified image 222 following edgedetection (as well as scaling and filtering as described above); themodified image 222 can correspond to the source edge image 112 of FIG.1, for example. Edge pixels are white, and non-edge pixels are black.Although not separately shown, it is understood that a similarcounterpart image of edge pixels is obtained from the counterpart framein the other show (e.g., derivative version or original) being compared,such as corresponding to the source edge image 113 of FIG. 1.

Returning to FIG. 1, with the two edge images 112 and 113, a search ofthe matching pixels is made as represented by an image differencedetector (search logic) 114. In one implementation, the search windowcan be adjusted to broaden the search area, e.g., by comparing a pixelagainst a counterpart pixel and one or more of its neighboring pixels.Making the search too broad reduces the ability to find small changes inthe image, but can be useful in some scenarios, depending on what isbeing looked for in the two images.

In one implementation, the difference between the two images can berepresented by a score, generally by counting edge pixels that match andedge pixels that did not match, and obtaining a ratio (e.g., an errorpercentage). The score for the frame pairs can be maintained in an imagedifference report 116. Note that rather than evaluating a fixednon-matching value versus a count threshold value, the ratio/errorpercentage generally provides a more useful indicator, because someimages have a lot of edges (and thus likely more mismatches) while someimages do not have many edges (and thus likely less mismatches).

The error percentage is then evaluated against a threshold ratio value.If the error percentage is above the threshold ratio value, the framecorresponding to the difference image (e.g., the counterpart frame) canbe flagged as an error candidate, (e.g., based on data in the finalimage difference report, and/or in a graphical representation thereof)for further review.

As an added benefit to a reviewer, if the error percentage is above thethreshold ratio value, the image can be further processed to show thereviewer what has made this image fail. For example, the target(secondary) source can be overlaid with the matched and unmatched testresults, with red pixels highlighting the error detected. FIG. 3 shows afull image 333 (generally corresponding to FIG. 2) that has failed dueto font differences; indeed, only the graphic is bad, as white pixelsare matching pixels. The overlaid images can be created on demand,and/or can be automatically generated and included in or associated withthe image difference report 116.

As can be readily appreciated, any of the operations of the blocks inFIG. 1 can be performed in advance or in parallel, at least to anextent, possibly on multiple devices. For example, a show can be brokenup into many short segments and with multithreading and/or paralleldevices, the processing/logic described herein allow working on thoseseparate segments at the same time. The various reported data can besorted back into frame order to obtain a final report.

FIGS. 4 and 5 show example operations similar to those performed by thecomponents of FIG. 1. As set forth herein, the operations of FIGS. 4 and5 can be applied to an entire show, or on some short segment or segmentsof a show in parallel with similar operations by other threads/devices.

Operation 402 of FIG. 4 represents selecting the first frame pair, oneframe from the original show and one frame from the derivative version.Operation 404 decodes those frames into images, e.g., images A and B.Operation 406 represents preparing those images, such as by scaling,luminance extraction and filtering as described herein. Note that anypart of operation 406 can be repeated and tweaked as desired, whetherautomatically or manually, or a combination of both, until desiredimages are obtained; however as described herein, this is typically onlydone once and applies to the subsequent images of the short segmentbeing processed or possibly the entire show.

Operation 408 represents performing the edge detection. At this time,both images comprise a combination of edge pixels (e.g., white) andnon-edge pixels (e.g., black). The process then continues to operation502 of FIG. 5.

Operation 502 represents performing the edge pixel matching of the imagewith the counterpart image (operations 504 and 506) to build a new imageC containing edge pixel matches (e.g., white pixels) and edge pixelmismatches (e.g., red pixels), as well as non-edge pixels (blackpixels). In general, one image is selected as the source image, and foreach edge pixel, a search is performed on the counterpart image todetermine if there is a match or mismatch, which is then used togenerate the new image C. An example of searching to build a new imageis detailed in FIGS. 6 and 7.

Operations 508 and 510 represent scanning the new image to count matchesand mismatches to obtain an error percentage ratio) as described herein.This information is saved to a file, such as a comma separated value(.csv) file for importing into a spreadsheet or the like. If the errorpercentage is greater than the error threshold, which a human operatorcan configure, the new image C is associated with the frame number forfurther review. Note that this is only one example implementation, and,for example, the generated image data with errors can be maintained forat least some images that do not meet the threshold error percentage, incase the operator wants to modify the threshold error percentage withoutre-running the entire process.

The process exemplified in FIGS. 4 and 5 returns to operation 414 torepeat for the next frame (operation 416) until there are no more framesin this segment (which can be the entire show if parallel operations arenot performed). Operation 418 represents sorting the reports, which canwait until other (parallel processed) segments have similarly completed.To this end, for example, the various individual reports can be writteninto a single file, e.g., which can be imported into a spreadsheet forsorting by frame number. The results for the frames can be graphed,showing where errors spike and/or tend to congregate, e.g., for a largerscene having errors. The graph can be interactive, allowing a user tonavigate through the frames of the two video shows side-by-side, as wellas view the new error image C created based on those frames, by slidingover the graph, for example. A frame number (or timecode) entry fieldcan also be used to jump directly to the two paired frames and theirrelated error image.

FIGS. 6 and 7 show additional details of one example searchimplementation in which a selected edge pixel from one image is comparedagainst a counterpart pixel in the other image, along with thecounterpart pixel's neighboring pixels. In this example the searchwindow area expands to evaluate the neighbors, namely the upper leftpixel, upper pixel, upper right pixel, left pixel, right pixel, lowerleft pixel, lower pixel and lower right pixel. As is understood, thissearch window area can be increased or decreased relative to thisexample. As is also understood, the searching operations create a newimage C, which can be initially set to one color (e.g., all black) orleft transparent, with matches resulting in a white pixel being writtenand mismatches resulting in a red pixel being written.

Operation 602 moves to set the first pixel in the source frame as thecurrent position. Note that a region of interest can be defined by theoperator. In this way, the window of what is deemed important to reviewcan be made to be less than the entire frame. Alternatively, a frame canbe searched (e.g., with a different set of neighboring pixels) and/orscored differently (e.g., increment the match or mismatch counter by oneif outside the region or two points inside the region) inside a definedregion of interest (a “safe title area”) versus outside the region ofinterest, for example. Setting for both percentage of width andpercentage of height allows the operator to ignore the “edge” of aframe, or change how the edge area is searched and/or scored. Further,even if the “entire” frame is to be searched, an offset based on thenumber of neighboring counterpart can be used so that the search for amatching pixel never extends outside the counterpart frame boundary.

Operation 604 selects the pixel from the current position in the sourceframe. If the selected pixel is not an edge pixel (e.g., is not a whitepixel) as evaluated at operation 606, the search is bypassed, with thecurrent position modified and so on until the end of the frame boundarylimit is reached.

When an edge pixel is detected, operation 606 branches to operation 702of FIG. 7 where the counterpart pixel is selected. In this example, viasteps 704 through 718, any edge pixel in the selected counterpart pixelor its neighbors are considered a match. Note that although notexplicitly shown, any match can bypass further checks for a match.Alternatively, a simple coding operation can combine operations 606 and704-718, e.g., if source is an edge pixel AND (counterpart pixel ORupper left pixel OR upper pixel OR upper right pixel OR left pixel ORright pixel OR lower left pixel OR lower pixel OR lower right pixel) isan edge pixel then MATCH else MISMATCH.

Operation 720 evaluates for a match, and if so, operation 722 writes awhite pixel into the new image C. Otherwise, operation 724 writes a redpixel into the new image C, and the process returns to operation 608 ofFIG. 6.

As can be seen, operations 608 and 610 move the current position to thenext pixel in the horizontal direction until the right boundary, whichalong with the lower boundary can be set by the operator or by defaultas described herein, is reached. When reached, operations 612 and 614reset the horizontal start position and move to the next line down inthe vertical direction in this example. When both the horizontal andvertical limits have been reached, the search process is ended and thereporting process of FIG. 8 begins. Note that in this example searchscanning occurs from upper left to upper right and then down, howeverany search/scanning directions can be used to obtain the same result. Itis also feasible to sample (e.g., select every other pixel by moving thecurrent position by two instead of one), however sampling can result inless accurate results.

FIG. 8 represents the reporting process based on the new image C,beginning at operation 802 which initializes the match and mismatchcounters, e.g., to zero. Operation 804 moves the current position to thestarting upper left boundary, which is generally the first pixel becauseany region of interest was already determined when performing the searchand generating the new image C.

Operation 806 selects the pixel at the current position, and if a whitepixel at operation 808, increments the match counter at operation 810.If not a white pixel, operation 812 evaluates for a red pixel, and ifred, increments the mismatch counter at operation 814. If neither rednor white, then the pixel is not an edge pixel and the processcontinues.

Operation 816 evaluates for the horizontal limit, ordinarily the lastpixel in the horizontal direction in the new image C, and if notreached, moves the current position to the next pixel to the right.Otherwise operation 820 evaluates if the last pixel in the verticaldirection had been reached, and if not, moves to the next line down andto the first pixel horizontally in the new line. As can be seen, each ofthe pixels in the new image are thus evaluated for white/match orred/mismatch, with the corresponding counters appropriately adjusted.

When finished, operation 824 outputs the results, which for example caninclude the frame number, the edge pixel count, non-edge pixel count,the mismatch count, the match count and/or the error percentage. Notethat the counts themselves can be useful, e.g., even if frame pair has alower error percentage that does not meet the threshold error level, analternative implementation can further look for a very high mismatchcount and flag that frame for further review.

In this way, the frame pairs are evaluated as source, counterpart edgepixel images for mismatches/errors. By using edges, the technologyoperates on frame content, generally ignoring color space and/or EOTFwhen making the comparison. The new image can be evaluated, or used asan overlay with an existing actual frame to highlight the differences asin FIG. 3.

One or more aspects can be embodied in a system, such as represented inFIG. 9, and for example can comprise a memory that stores computerexecutable components and/or operations, and a processor that executescomputer executable components and/or operations stored in the memory.Example operations can comprise operation 902, which representsobtaining a source frame from a first group of frames. Operation 904represents obtaining a counterpart frame from a second group of frames.Operation 906 represents processing the source frame into a source imagecomprising edge pixels and non-edge pixels. Operation 908 representsprocessing the counterpart frame into a counterpart image comprisingedge pixels and non-edge pixels. Operation 910 represents determiningmismatched edge pixels in the counterpart image that do not matchcorresponding edge pixels of the source image. Operation 912 representsreporting on the mismatched edge pixels.

Determining the mismatched edge pixels in the counterpart image that donot match edge pixels of the source image can comprise, for an edgepixel of the source image, searching a corresponding pixel in thecounterpart image to look for an edge pixel.

Determining the mismatched edge pixels in the counterpart image that donot match edge pixels of the source image can comprise, for an edgepixel of the source image, searching a corresponding pixel and at leastone neighboring pixel of the corresponding pixel in the counterpartimage to look for an edge pixel.

Determining the mismatched edge pixels in the counterpart image that donot match edge pixels of the source image can comprise generating adifference image of matched pixels and mismatched pixels. Reporting onthe mismatched edge pixels can comprise counting the mismatched pixelsin the difference image. Further operations can comprise evaluating anerror percentage, based on the counting the mismatched pixels in thedifference image, against a threshold error percentage, and if thethreshold error percentage is exceeded, flagging the counterpart frameas an error candidate.

Processing the source frame into the source image can comprise decodingthe source frame into the source image, and wherein the processing thecounterpart frame into the counterpart image can comprise decoding thecounterpart frame into the counterpart image.

Further operations can comprise scaling the counterpart image or scalingthe source image. Further operations can comprise performing luminanceextraction on the source image and performing luminance extraction onthe counterpart image. Further operations can comprise filtering thesource image and filtering the counterpart image.

Determining the mismatched edge pixels in the counterpart image comprisesearching for mismatched edge pixels in a defined region of interest.

The first group of frames can comprise a segment of frames of anoriginal show, and the second group of frames can comprise acorresponding segment of frames of a derivative of the original show.

One or more example aspects, such as corresponding to operations of amethod, are represented in FIG. 10. Operation 1002 representsconverting, via a system comprising a processor, frames of originalvideo content into first edge pixel images, and converting correspondingframes of video content derived from the original video content intocorresponding second edge pixel images. Operation 1004 representsselecting a source edge pixel image from the first edge pixel images,and selecting a corresponding edge pixel image from the second images.Operation 1006 represents comparing edge pixels in the source edge pixelimage with edge pixels in the corresponding edge pixel image todetermine mismatched edge pixels. Operation 1008 represents reporting onthe mismatched edge pixels.

Aspects can comprise generating a difference image that differentiatesthe mismatched edge pixels from matched edge pixels.

Comparing the edge pixels in the source edge pixel image with the edgepixels in the corresponding edge pixel image to determine the mismatchededge pixels can comprise selecting at least some edge pixels from thesource edge pixel image, and for each selected edge pixel, searching thecorresponding edge pixel image in a search window based on a counterpartpixel to determine whether the search window contains an edge pixel. Thesearch window can comprise the counterpart pixel and at least oneneighbor pixel to the counterpart pixel.

FIG. 11 summarizes various example operations, e.g., corresponding toexecutable instructions of a machine-readable storage medium, in whichthe executable instructions, when executed by a processor, facilitateperformance of the example operations. Operation 1102 representsprocessing first image data into a first edge pixel image. Operation1104 represents processing second image data into a second edge pixelimage. Operation 1106 represents selecting a selected edge pixel fromthe first edge pixel image. Operation 1108 represents comparing theselected edge pixel with a search window, based on a counterpart pixelin the second edge pixel image, to determine whether the selected edgepixel is a match having a matching edge pixel in the search window orwhether the selected edge pixel is a mismatch having no matching edgepixel in the search window. Operation 1110 represents repeating theselecting and comparing for at least some other edge pixels in the firstedge pixel image.

Further operations can comprise reporting information corresponding tomatch data or mismatch data, or both and match and mismatch data.

Further operations can comprise preparing the first image data,comprising at least one of: performing scaling, performing luminanceextraction or performing filtering of source data into the first imagedata.

Further operations can comprise generating a difference image thatdifferentiates which of the selected edge pixels has a matching edgepixel in the search window and which of the selected edge pixels is amismatch having no matching edge pixel in the search window.

As can be seen, the described technology operates to detect contentdifferences in a manner that ignores color space and/or EOTF differenceswhen making the difference comparison. Items such as font, object orperson scale and position are compared by reducing the image to edges.Those edges are then compared to see if they are overlaying andmatching. The difference is scored and if the error percentage is toohigh, the image is logged and stored for review by a person to eitherconfirm the error or it was a false error. The total amount of workperformed is significantly reduced relative to manual methods, with onlysuspected frames needing to be reviewed. The sensitivity of thematch/error can be adjusted by a threshold setting. A score can bedetermined by noting a ratio of matching versus unmatched pixels.

Because the described technology operates on luminance and edges, thesources can be from almost any source, e.g., DPX, IMF, AS02, Op1a, andany color and EOTF domain. The technology also may be used to determinewhether the artifacts in a compressed show can be scored. By changingthe threshold and filter, such artifacts can be detected. For example,if the compression had a fault, the image is altered, and the detectioncan catch this event. If a frame is dropped or repeated it too can becaught.

The techniques described herein can be applied to any device or set ofdevices (machines) capable of running programs and processes. It can beunderstood, therefore, that personal computers, laptops, handheld,portable and other computing devices and computing objects of all kindsincluding cell phones, tablet/slate computers, gaming/entertainmentconsoles and the like are contemplated for use in connection withvarious implementations including those exemplified herein. Accordingly,the general purpose computing mechanism described below in FIG. 12 isbut one example of a computing device.

Implementations can partly be implemented via an operating system, foruse by a developer of services for a device or object, and/or includedwithin application software that operates to perform one or morefunctional aspects of the various implementations described herein.Software may be described in the general context of computer executableinstructions, such as program modules, being executed by one or morecomputers, such as client workstations, servers or other devices. Thoseskilled in the art will appreciate that computer systems have a varietyof configurations and protocols that can be used to communicate data,and thus, no particular configuration or protocol is consideredlimiting.

FIG. 12 thus illustrates a schematic block diagram of a computingenvironment 1200 with which the disclosed subject matter can interact.The system 1200 comprises one or more remote component(s) 1210. Theremote component(s) 1210 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, remote component(s)1210 can be a distributed computer system, connected to a localautomatic scaling component and/or programs that use the resources of adistributed computer system, via communication framework 1240.Communication framework 1240 can comprise wired network devices,wireless network devices, mobile devices, wearable devices, radio accessnetwork devices, gateway devices, femtocell devices, servers, etc.

The system 1200 also comprises one or more local component(s) 1220. Thelocal component(s) 1220 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)1220 can comprise an automatic scaling component and/or programs thatcommunicate/use the remote resources 1210 and 1220, etc., connected to aremotely located distributed computing system via communicationframework 1240.

One possible communication between a remote component(s) 1210 and alocal component(s) 1220 can be in the form of a data packet adapted tobe transmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 1210 and a localcomponent(s) 1220 can be in the form of circuit-switched data adapted tobe transmitted between two or more computer processes in radio timeslots. The system 1200 comprises a communication framework 1240 that canbe employed to facilitate communications between the remote component(s)1210 and the local component(s) 1220, and can comprise an air interface,e.g., Uu interface of a UMTS network, via a long-term evolution (LTE)network, etc. Remote component(s) 1210 can be operably connected to oneor more remote data store(s) 1250, such as a hard drive, solid statedrive, SIM card, device memory, etc., that can be employed to storeinformation on the remote component(s) 1210 side of communicationframework 1240. Similarly, local component(s) 1220 can be operablyconnected to one or more local data store(s) 1230, that can be employedto store information on the local component(s) 1220 side ofcommunication framework 1240.

In order to provide additional context for various embodiments describedherein, FIG. 13 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1300 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 13, the example environment 1300 forimplementing various embodiments of the aspects described hereinincludes a computer 1302, the computer 1302 including a processing unit1304, a system memory 1306 and a system bus 1308. The system bus 1308couples system components including, but not limited to, the systemmemory 1306 to the processing unit 1304. The processing unit 1304 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1304.

The system bus 1308 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1306includes ROM 1310 and RAM 1312. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1302, such as during startup. The RAM 1312 can also include a high-speedRAM such as static RAM for caching data.

The computer 1302 further includes an internal hard disk drive (HDD)1314 (e.g., EIDE, SATA), and can include one or more external storagedevices 1316 (e.g., a magnetic floppy disk drive (FDD) 1316, a memorystick or flash drive reader, a memory card reader, etc.). While theinternal HDD 1314 is illustrated as located within the computer 1302,the internal HDD 1314 can also be configured for external use in asuitable chassis (not shown). Additionally, while not shown inenvironment 1300, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1314.

Other internal or external storage can include at least one otherstorage device 1320 with storage media 1322 (e.g., a solid state storagedevice, a nonvolatile memory device, and/or an optical disk drive thatcan read or write from removable media such as a CD-ROM disc, a DVD, aBD, etc.). The external storage 1316 can be facilitated by a networkvirtual machine. The HDD 1314, external storage device(s) 1316 andstorage device (e.g., drive) 1320 can be connected to the system bus1308 by an HDD interface 1324, an external storage interface 1326 and adrive interface 1328, respectively.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1302, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1312,including an operating system 1330, one or more application programs1332, other program modules 1334 and program data 1336. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1312. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1302 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1330, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 13. In such an embodiment, operating system 1330 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1302.Furthermore, operating system 1330 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1332. Runtime environments are consistent executionenvironments that allow applications 1332 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1330can support containers, and applications 1332 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1302 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1302, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1302 throughone or more wired/wireless input devices, e.g., a keyboard 1338, a touchscreen 1340, and a pointing device, such as a mouse 1342. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1304 through an input deviceinterface 1344 that can be coupled to the system bus 1308, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1346 or other type of display device can be also connected tothe system bus 1308 via an interface, such as a video adapter 1348. Inaddition to the monitor 1346, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1302 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1350. The remotecomputer(s) 1350 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1302, although, for purposes of brevity, only a memory/storage device1352 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1354 and/orlarger networks, e.g., a wide area network (WAN) 1356. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1302 can beconnected to the local network 1354 through a wired and/or wirelesscommunication network interface or adapter 1358. The adapter 1358 canfacilitate wired or wireless communication to the LAN 1354, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1358 in a wireless mode.

When used in a WAN networking environment, the computer 1302 can includea modem 1360 or can be connected to a communications server on the WAN1356 via other means for establishing communications over the WAN 1356,such as by way of the Internet. The modem 1360, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1308 via the input device interface 1344. In a networkedenvironment, program modules depicted relative to the computer 1302 orportions thereof, can be stored in the remote memory/storage device1352. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1302 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1316 asdescribed above. Generally, a connection between the computer 1302 and acloud storage system can be established over a LAN 1354 or WAN 1356e.g., by the adapter 1358 or modem 1360, respectively. Upon connectingthe computer 1302 to an associated cloud storage system, the externalstorage interface 1326 can, with the aid of the adapter 1358 and/ormodem 1360, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1326 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1302.

The computer 1302 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or a firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances.

While the embodiments are susceptible to various modifications andalternative constructions, certain illustrated implementations thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit thevarious embodiments to the specific forms disclosed, but on thecontrary, the intention is to cover all modifications, alternativeconstructions, and equivalents falling within the spirit and scope.

In addition to the various implementations described herein, it is to beunderstood that other similar implementations can be used ormodifications and additions can be made to the describedimplementation(s) for performing the same or equivalent function of thecorresponding implementation(s) without deviating therefrom. Stillfurther, multiple processing chips or multiple devices can share theperformance of one or more functions described herein, and similarly,storage can be effected across a plurality of devices. Accordingly, thevarious embodiments are not to be limited to any single implementation,but rather is to be construed in breadth, spirit and scope in accordancewith the appended claims.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, the operationscomprising: obtaining a source frame from a first group of frames;obtaining a counterpart frame from a second group of frames; processingthe source frame into a source image comprising edge pixels and non-edgepixels; processing the counterpart frame into a counterpart imagecomprising edge pixels and non-edge pixels; determining mismatched edgepixels in the counterpart image that do not match corresponding edgepixels of the source image, comprising generating a difference image ofmatched pixels and mismatched pixels; reporting on the mismatched edgepixels, comprising counting the mismatched pixels in the differenceimage; and evaluating an error percentage, based on the counting themismatched pixels in the difference image, against a threshold errorpercentage, and if the threshold error percentage is exceeded, flaggingthe counterpart frame as an error candidate.
 2. The system of claim 1,wherein the determining the mismatched edge pixels in the counterpartimage that do not match edge pixels of the source image comprises, foran edge pixel of the source image, searching a corresponding pixel inthe counterpart image to look for an edge pixel.
 3. The system of claim1, wherein the determining the mismatched edge pixels in the counterpartimage that do not match edge pixels of the source image comprises, foran edge pixel of the source image, searching a corresponding pixel andat least one neighboring pixel of the corresponding pixel in thecounterpart image to look for an edge pixel.
 4. The system of claim 1,wherein the processing the source frame into the source image comprisesdecoding the source frame into the source image, and wherein theprocessing the counterpart frame into the counterpart image comprisesdecoding the counterpart frame into the counterpart image.
 5. The systemof claim 1, wherein the operations further comprise scaling thecounterpart image or scaling the source image.
 6. The system of claim 1,wherein the operations further comprise performing luminance extractionon the source image and performing luminance extraction on thecounterpart image.
 7. The system of claim 1, wherein the operationsfurther comprise filtering the source image and filtering thecounterpart image.
 8. The system of claim 1, wherein the determining themismatched edge pixels in the counterpart image comprise searching formismatched edge pixels in a defined region of interest.
 9. The system ofclaim 1, wherein the first group of frames comprises a segment of framesof an original show, and wherein the second group of frames comprises acorresponding segment of frames of a derivative of the original show.10. A method comprising: converting, via a system comprising aprocessor, frames of original video content into first edge pixelimages, and converting corresponding frames of video content derivedfrom the original video content into corresponding second edge pixelimages; selecting a source edge pixel image from the first edge pixelimages, and selecting a corresponding edge pixel image from the secondimages; comparing edge pixels in the source edge pixel image with edgepixels in the corresponding edge pixel image to determine mismatchededge pixels, comprising generating a difference image of matched pixelsand mismatched pixels; reporting on the mismatched edge pixels,comprising counting the mismatched pixels in the difference image; andevaluating an error percentage, based on the counting the mismatchedpixels in the difference image, against a threshold error percentage,and if the threshold error percentage is exceeded, flagging thecounterpart frame as an error candidate.
 11. The method of claim 10,wherein the generating the difference image differentiates themismatched edge pixels from matched edge pixels.
 12. The method of claim10, wherein the comparing the edge pixels in the source edge pixel imagewith the edge pixels in the corresponding edge pixel image to determinethe mismatched edge pixels comprises selecting at least some edge pixelsfrom the source edge pixel image, and for each selected edge pixel,searching the corresponding edge pixel image in a search window based ona counterpart pixel to determine whether the search window contains anedge pixel.
 13. The method of claim 12, wherein the search windowcomprises the counterpart pixel and at least one neighbor pixel to thecounterpart pixel.
 14. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations, the operations comprising:processing first image data into a first edge pixel image; processingsecond image data into a second edge pixel image; selecting a selectededge pixel from the first edge pixel image; comparing the selected edgepixel with a search window, based on a counterpart pixel in the secondedge pixel image, to determine whether the selected edge pixel is amatch having a matching edge pixel in the search window or whether theselected edge pixel is a mismatch having no matching edge pixel in thesearch window; repeating the selecting and comparing for at least someother edge pixels in the first edge pixel image; determining mismatchededge pixels in the counterpart image that do not match correspondingedge pixels of the source image, comprising generating a differenceimage of matched pixels and mismatched pixels; reporting on themismatched edge pixels, comprising counting the mismatched pixels in thedifference image; and evaluating an error percentage, based on thecounting the mismatched pixels in the difference image, against athreshold error percentage, and if the threshold error percentage isexceeded, flagging the counterpart frame as an error candidate.
 15. Thenon-transitory machine-readable medium of claim 14, wherein theoperations further comprise reporting information corresponding to matchdata or mismatch data, or both and match and mismatch data.
 16. Thenon-transitory machine-readable storage medium of claim 14, wherein theoperations further comprise preparing the first image data, comprisingat least one of: performing scaling, performing luminance extraction orperforming filtering of source data into the first image data.
 17. Thenon-transitory machine-readable medium of claim 14, wherein thedifference image differentiates which of the selected edge pixels has amatching edge pixel in the search window and which of the selected edgepixels is a mismatch having no matching edge pixel in the search window.18. The non-transitory machine-readable medium of claim 14, wherein theoperations further comprise at least one of: scaling the first imagedata or scaling the second image data.
 19. The non-transitorymachine-readable medium of claim 14, wherein the operations furthercomprise at least one of: performing luminance extraction on the firstimage data or performing luminance extraction on the second image data.20. The non-transitory machine-readable medium of claim 14, wherein theoperations further comprise at least one of: filtering the first imagedata and filtering the second image data.